How I treat Philadelphia chromosome–like acute lymphoblastic leukemia in children, adolescents, and young adults
Bibliographic record
Abstract
ABSTRACT: Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL) represents a high-risk B-lineage ALL subtype characterized by adverse clinical features and poor relapse-free survival despite risk-adapted multiagent chemotherapy regimens. The advent of next-generation sequencing has unraveled the diversity of kinase-activating genetic drivers in Ph-like ALL that are potentially amenable to personalized molecularly-targeted therapies. Based upon robust preclinical data and promising case series of clinical activity of tyrosine kinase inhibitor (TKI)-based treatment in adults and children with relevant genetic Ph-like ALL subtypes, several clinical trials have investigated the efficacy of JAK- or ABL-directed TKIs in cytokine receptor-like factor 2 (CRLF2)/JAK pathway-mutant or ABL-class Ph-like ALL, respectively. The final results of these trials are pending, and standard-of-care therapeutic approaches for patients with Ph-like ALL have yet to be defined. In this How I Treat perspective, we review recent literature to guide current evidence-based treatment recommendations via illustrative clinical vignettes of children, adolescents, and young adults with newly diagnosed or relapsed/refractory Ph-like ALL, and we further highlight open and soon-to-open trials investigating immunotherapy and TKIs specifically for this high-risk patient population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".